Instructions to use ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO")
model = AutoModelForCausalLM.from_pretrained("ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO

SGLang

How to use ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO with Docker Model Runner:
```
docker model run hf.co/ethicalabs/Kurtis-SmolLM2-135M-Instruct-DPO
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

⚠️ Disclaimer: Model Limitations & Retraining Plans

While this experiment aimed to explore the feasibility of small, local AI assistants, the current model struggles with generalization and often reinforces patterns from training data rather than adapting dynamically.

To address this, we will repeat the fine-tuning process, refining the dataset and training approach to improve response accuracy and adaptability.

The goal remains the same: a reliable, privacy-first AI assistant that runs locally on edge devices.

Stay tuned for updates as we iterate and improve! 🚀

Model Card for Kurtis

Kurtis is a mental-health AI assistant designed with empathy at its core.

Unlike other AI models that aim for peak efficiency, Kurtis prioritizes understanding, emotional nuance, and meaningful conversations.

It won’t solve complex math problems or write code, nor will it generate images or videos.

Instead, Kurtis focuses on being a thoughtful companion, offering support, perspective, and human-like dialogue.

It doesn’t strive to break records or chase artificial intelligence supremacy—its goal is to create a space for genuine interaction.

Whether you need someone to talk to, reflect on ideas with, or engage in insightful discussion, Kurtis is there to listen and respond in an understanding way.